Overview

Dataset statistics

Number of variables16
Number of observations47935
Missing cells288011
Missing cells (%)37.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 MiB
Average record size in memory128.0 B

Variable types

Categorical6
Numeric9
Unsupported1

Alerts

Unnamed: 10 has constant value "#NAME?"Constant
Unnamed: 11 has constant value "#NAME?"Constant
country has a high cardinality: 204 distinct valuesHigh cardinality
pub_id is highly overall correlated with Unnamed: 13 and 1 other fieldsHigh correlation
app_id is highly overall correlated with Unnamed: 13 and 1 other fieldsHigh correlation
ad_unit_code is highly overall correlated with Unnamed: 15 and 2 other fieldsHigh correlation
requests is highly overall correlated with ae_responses and 3 other fieldsHigh correlation
ae_responses is highly overall correlated with requests and 4 other fieldsHigh correlation
ae_impressions is highly overall correlated with requests and 5 other fieldsHigh correlation
ae_clicks is highly overall correlated with ae_impressions and 3 other fieldsHigh correlation
ae_revenue is highly overall correlated with ae_responses and 4 other fieldsHigh correlation
Unnamed: 15 is highly overall correlated with ad_unit_code and 3 other fieldsHigh correlation
date is highly overall correlated with Unnamed: 15 and 2 other fieldsHigh correlation
Unnamed: 13 is highly overall correlated with pub_id and 10 other fieldsHigh correlation
Unnamed: 14 is highly overall correlated with pub_id and 10 other fieldsHigh correlation
Unnamed: 10 has 47934 (> 99.9%) missing valuesMissing
Unnamed: 11 has 47934 (> 99.9%) missing valuesMissing
Unnamed: 12 has 47935 (100.0%) missing valuesMissing
Unnamed: 13 has 47929 (> 99.9%) missing valuesMissing
Unnamed: 14 has 47930 (> 99.9%) missing valuesMissing
Unnamed: 15 has 47929 (> 99.9%) missing valuesMissing
requests is highly skewed (γ1 = 21.74933334)Skewed
ae_responses is highly skewed (γ1 = 22.84978943)Skewed
ae_impressions is highly skewed (γ1 = 21.14684899)Skewed
ae_clicks is highly skewed (γ1 = 38.07757129)Skewed
ae_revenue is highly skewed (γ1 = 42.19101501)Skewed
Unnamed: 13 is uniformly distributedUniform
Unnamed: 14 is uniformly distributedUniform
Unnamed: 12 is an unsupported type, check if it needs cleaning or further analysisUnsupported
ae_clicks has 14839 (31.0%) zerosZeros

Reproduction

Analysis started2023-04-14 17:23:12.539128
Analysis finished2023-04-14 17:23:35.296626
Duration22.76 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

date
Categorical

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size374.6 KiB
07-10-2022
 
1783
08-10-2022
 
1758
06-10-2022
 
1737
02-10-2022
 
1724
05-10-2022
 
1711
Other values (26)
39222 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters479350
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20-10-2022
2nd row20-10-2022
3rd row20-10-2022
4th row20-10-2022
5th row20-10-2022

Common Values

ValueCountFrequency (%)
07-10-2022 1783
 
3.7%
08-10-2022 1758
 
3.7%
06-10-2022 1737
 
3.6%
02-10-2022 1724
 
3.6%
05-10-2022 1711
 
3.6%
04-10-2022 1686
 
3.5%
09-10-2022 1676
 
3.5%
03-10-2022 1667
 
3.5%
01-10-2022 1613
 
3.4%
19-10-2022 1570
 
3.3%
Other values (21) 31010
64.7%

Length

2023-04-14T22:53:35.566577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
07-10-2022 1783
 
3.7%
08-10-2022 1758
 
3.7%
06-10-2022 1737
 
3.6%
02-10-2022 1724
 
3.6%
05-10-2022 1711
 
3.6%
04-10-2022 1686
 
3.5%
09-10-2022 1676
 
3.5%
03-10-2022 1667
 
3.5%
01-10-2022 1613
 
3.4%
19-10-2022 1570
 
3.3%
Other values (21) 31010
64.7%

Most occurring characters

ValueCountFrequency (%)
2 163596
34.1%
0 115674
24.1%
- 95870
20.0%
1 68460
14.3%
3 7501
 
1.6%
9 4764
 
1.0%
8 4758
 
1.0%
5 4729
 
1.0%
7 4697
 
1.0%
4 4668
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 383480
80.0%
Dash Punctuation 95870
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 163596
42.7%
0 115674
30.2%
1 68460
17.9%
3 7501
 
2.0%
9 4764
 
1.2%
8 4758
 
1.2%
5 4729
 
1.2%
7 4697
 
1.2%
4 4668
 
1.2%
6 4633
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 95870
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 479350
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 163596
34.1%
0 115674
24.1%
- 95870
20.0%
1 68460
14.3%
3 7501
 
1.6%
9 4764
 
1.0%
8 4758
 
1.0%
5 4729
 
1.0%
7 4697
 
1.0%
4 4668
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 479350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 163596
34.1%
0 115674
24.1%
- 95870
20.0%
1 68460
14.3%
3 7501
 
1.6%
9 4764
 
1.0%
8 4758
 
1.0%
5 4729
 
1.0%
7 4697
 
1.0%
4 4668
 
1.0%

pub_id
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean413.18821
Minimum14
Maximum2808
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size374.6 KiB
2023-04-14T22:53:35.695231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile14
Q114
median51
Q372
95-th percentile2808
Maximum2808
Range2794
Interquartile range (IQR)58

Descriptive statistics

Standard deviation893.64566
Coefficient of variation (CV)2.1628053
Kurtosis2.9881305
Mean413.18821
Median Absolute Deviation (MAD)37
Skewness2.1893061
Sum19806177
Variance798602.56
MonotonicityNot monotonic
2023-04-14T22:53:35.803942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
14 17349
36.2%
51 14949
31.2%
2808 5442
 
11.4%
72 4906
 
10.2%
281 2317
 
4.8%
879 1605
 
3.3%
107 557
 
1.2%
1987 521
 
1.1%
34 289
 
0.6%
ValueCountFrequency (%)
14 17349
36.2%
34 289
 
0.6%
51 14949
31.2%
72 4906
 
10.2%
107 557
 
1.2%
281 2317
 
4.8%
879 1605
 
3.3%
1987 521
 
1.1%
2808 5442
 
11.4%
ValueCountFrequency (%)
2808 5442
 
11.4%
1987 521
 
1.1%
879 1605
 
3.3%
281 2317
 
4.8%
107 557
 
1.2%
72 4906
 
10.2%
51 14949
31.2%
34 289
 
0.6%
14 17349
36.2%

app_id
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50155772
Minimum16243270
Maximum95674771
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size374.6 KiB
2023-04-14T22:53:35.923622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16243270
5-th percentile22693095
Q125596802
median47862278
Q347862278
95-th percentile95674771
Maximum95674771
Range79431501
Interquartile range (IQR)22265476

Descriptive statistics

Standard deviation24657717
Coefficient of variation (CV)0.49162273
Kurtosis-0.70216238
Mean50155772
Median Absolute Deviation (MAD)22265476
Skewness0.68752648
Sum2.4042169 × 1012
Variance6.0800303 × 1014
MonotonicityIncreasing
2023-04-14T22:53:36.044300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
47862278 14949
31.2%
22693095 11291
23.6%
95674771 6058
12.6%
43840611 5442
 
11.4%
84650721 4906
 
10.2%
40931528 2317
 
4.8%
25596802 1605
 
3.3%
63338696 557
 
1.2%
16243270 521
 
1.1%
66981243 289
 
0.6%
ValueCountFrequency (%)
16243270 521
 
1.1%
22693095 11291
23.6%
25596802 1605
 
3.3%
40931528 2317
 
4.8%
43840611 5442
 
11.4%
47862278 14949
31.2%
63338696 557
 
1.2%
66981243 289
 
0.6%
84650721 4906
 
10.2%
95674771 6058
12.6%
ValueCountFrequency (%)
95674771 6058
12.6%
84650721 4906
 
10.2%
66981243 289
 
0.6%
63338696 557
 
1.2%
47862278 14949
31.2%
43840611 5442
 
11.4%
40931528 2317
 
4.8%
25596802 1605
 
3.3%
22693095 11291
23.6%
16243270 521
 
1.1%

ad_unit_code
Real number (ℝ)

Distinct56
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2684021 × 1010
Minimum2.2477474 × 1010
Maximum2.283576 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size374.6 KiB
2023-04-14T22:53:36.259721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.2477474 × 1010
5-th percentile2.2477733 × 1010
Q12.2521511 × 1010
median2.275208 × 1010
Q32.2770341 × 1010
95-th percentile2.2827807 × 1010
Maximum2.283576 × 1010
Range3.5828642 × 108
Interquartile range (IQR)2.488309 × 108

Descriptive statistics

Standard deviation1.215869 × 108
Coefficient of variation (CV)0.0053600243
Kurtosis-1.1893423
Mean2.2684021 × 1010
Median Absolute Deviation (MAD)54282040
Skewness-0.6484422
Sum1.0873585 × 1015
Variance1.4783375 × 1016
MonotonicityNot monotonic
2023-04-14T22:53:36.445264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.275263244 × 10104808
 
10.0%
2.275208048 × 10104617
 
9.6%
2.277034143 × 10102553
 
5.3%
2.269779844 × 10102485
 
5.2%
2.275208067 × 10101866
 
3.9%
2.278852573 × 10101801
 
3.8%
2.277034105 × 10101745
 
3.6%
2.252151044 × 10101692
 
3.5%
2.27703416 × 10101647
 
3.4%
2.252150936 × 10101614
 
3.4%
Other values (46) 23107
48.2%
ValueCountFrequency (%)
2.24774737 × 10101525
3.2%
2.247773309 × 1010994
2.1%
2.247773311 × 1010815
1.7%
2.247773316 × 1010107
 
0.2%
2.248763942 × 10101186
2.5%
2.248763971 × 1010923
1.9%
2.248763972 × 1010848
1.8%
2.252149148 × 1010157
 
0.3%
2.252150936 × 10101614
3.4%
2.252151027 × 10101590
3.3%
ValueCountFrequency (%)
2.283576012 × 1010663
1.4%
2.283540832 × 10101182
2.5%
2.283284659 × 1010206
 
0.4%
2.283284644 × 1010226
 
0.5%
2.283282773 × 101089
 
0.2%
2.282780711 × 1010555
1.2%
2.282780606 × 1010624
1.3%
2.282747522 × 1010590
1.2%
2.282747502 × 1010548
1.1%
2.282064594 × 101096
 
0.2%

country
Categorical

Distinct204
Distinct (%)0.4%
Missing420
Missing (%)0.9%
Memory size374.6 KiB
India
 
1230
United States
 
974
Brazil
 
911
Saudi Arabia
 
879
Nigeria
 
878
Other values (199)
42643 

Length

Max length32
Median length22
Mean length8.1724087
Min length4

Characters and Unicode

Total characters388312
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowTanzania
2nd rowTanzania
3rd rowThailand
4th rowThailand
5th rowThailand

Common Values

ValueCountFrequency (%)
India 1230
 
2.6%
United States 974
 
2.0%
Brazil 911
 
1.9%
Saudi Arabia 879
 
1.8%
Nigeria 878
 
1.8%
France 812
 
1.7%
United Kingdom 811
 
1.7%
Pakistan 803
 
1.7%
South Africa 780
 
1.6%
Mexico 761
 
1.6%
Other values (194) 38676
80.7%

Length

2023-04-14T22:53:36.685594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united 2344
 
4.1%
india 1230
 
2.2%
south 1210
 
2.1%
states 974
 
1.7%
brazil 911
 
1.6%
saudi 879
 
1.5%
arabia 879
 
1.5%
nigeria 878
 
1.5%
france 812
 
1.4%
kingdom 811
 
1.4%
Other values (227) 46281
80.9%

Most occurring characters

ValueCountFrequency (%)
a 60619
15.6%
i 35875
 
9.2%
n 29945
 
7.7%
e 28166
 
7.3%
r 21816
 
5.6%
o 17534
 
4.5%
t 16475
 
4.2%
d 14106
 
3.6%
l 13719
 
3.5%
u 11467
 
3.0%
Other values (47) 138590
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 320884
82.6%
Uppercase Letter 56556
 
14.6%
Space Separator 9694
 
2.5%
Other Punctuation 385
 
0.1%
Open Punctuation 341
 
0.1%
Close Punctuation 341
 
0.1%
Dash Punctuation 111
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 60619
18.9%
i 35875
11.2%
n 29945
9.3%
e 28166
 
8.8%
r 21816
 
6.8%
o 17534
 
5.5%
t 16475
 
5.1%
d 14106
 
4.4%
l 13719
 
4.3%
u 11467
 
3.6%
Other values (16) 71162
22.2%
Uppercase Letter
ValueCountFrequency (%)
S 7183
12.7%
A 4992
 
8.8%
I 4211
 
7.4%
B 4100
 
7.2%
U 3681
 
6.5%
C 3650
 
6.5%
M 3442
 
6.1%
P 3224
 
5.7%
K 3070
 
5.4%
T 2853
 
5.0%
Other values (15) 16150
28.6%
Other Punctuation
ValueCountFrequency (%)
' 383
99.5%
. 2
 
0.5%
Space Separator
ValueCountFrequency (%)
9694
100.0%
Open Punctuation
ValueCountFrequency (%)
( 341
100.0%
Close Punctuation
ValueCountFrequency (%)
) 341
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 111
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 377440
97.2%
Common 10872
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 60619
16.1%
i 35875
 
9.5%
n 29945
 
7.9%
e 28166
 
7.5%
r 21816
 
5.8%
o 17534
 
4.6%
t 16475
 
4.4%
d 14106
 
3.7%
l 13719
 
3.6%
u 11467
 
3.0%
Other values (41) 127718
33.8%
Common
ValueCountFrequency (%)
9694
89.2%
' 383
 
3.5%
( 341
 
3.1%
) 341
 
3.1%
- 111
 
1.0%
. 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 388312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 60619
15.6%
i 35875
 
9.2%
n 29945
 
7.7%
e 28166
 
7.3%
r 21816
 
5.6%
o 17534
 
4.5%
t 16475
 
4.2%
d 14106
 
3.6%
l 13719
 
3.5%
u 11467
 
3.0%
Other values (47) 138590
35.7%

requests
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3783
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean585.82781
Minimum16
Maximum156435
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size374.6 KiB
2023-04-14T22:53:36.868094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile24
Q150
median109
Q3300
95-th percentile1960
Maximum156435
Range156419
Interquartile range (IQR)250

Descriptive statistics

Standard deviation3024.6865
Coefficient of variation (CV)5.1630982
Kurtosis683.38141
Mean585.82781
Median Absolute Deviation (MAD)74
Skewness21.749333
Sum28081656
Variance9148728.3
MonotonicityNot monotonic
2023-04-14T22:53:37.235153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 404
 
0.8%
28 404
 
0.8%
32 397
 
0.8%
30 389
 
0.8%
23 388
 
0.8%
35 383
 
0.8%
33 382
 
0.8%
25 382
 
0.8%
31 375
 
0.8%
41 374
 
0.8%
Other values (3773) 44057
91.9%
ValueCountFrequency (%)
16 158
0.3%
17 195
0.4%
18 277
0.6%
19 308
0.6%
20 340
0.7%
21 350
0.7%
22 370
0.8%
23 388
0.8%
24 350
0.7%
25 382
0.8%
ValueCountFrequency (%)
156435 1
< 0.1%
128311 1
< 0.1%
120564 1
< 0.1%
120331 1
< 0.1%
115997 1
< 0.1%
109421 1
< 0.1%
107968 1
< 0.1%
103295 1
< 0.1%
97698 1
< 0.1%
96027 1
< 0.1%

ae_responses
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3636
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean516.9182
Minimum16
Maximum156296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size374.6 KiB
2023-04-14T22:53:37.467491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile23
Q147
median100
Q3275
95-th percentile1816
Maximum156296
Range156280
Interquartile range (IQR)228

Descriptive statistics

Standard deviation2579.6957
Coefficient of variation (CV)4.9905299
Kurtosis805.17104
Mean516.9182
Median Absolute Deviation (MAD)67
Skewness22.849789
Sum24778474
Variance6654830
MonotonicityNot monotonic
2023-04-14T22:53:37.660016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 465
 
1.0%
31 434
 
0.9%
25 433
 
0.9%
32 431
 
0.9%
29 422
 
0.9%
27 420
 
0.9%
22 420
 
0.9%
23 416
 
0.9%
26 413
 
0.9%
40 408
 
0.9%
Other values (3626) 43673
91.1%
ValueCountFrequency (%)
16 177
0.4%
17 243
0.5%
18 326
0.7%
19 340
0.7%
20 390
0.8%
21 389
0.8%
22 420
0.9%
23 416
0.9%
24 377
0.8%
25 433
0.9%
ValueCountFrequency (%)
156296 1
< 0.1%
126743 1
< 0.1%
109177 1
< 0.1%
97635 1
< 0.1%
87806 1
< 0.1%
85822 1
< 0.1%
82148 1
< 0.1%
79930 1
< 0.1%
78464 1
< 0.1%
77374 1
< 0.1%

ae_impressions
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2448
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean257.05514
Minimum16
Maximum69448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size374.6 KiB
2023-04-14T22:53:37.898340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile17
Q127
median52
Q3140
95-th percentile839
Maximum69448
Range69432
Interquartile range (IQR)113

Descriptive statistics

Standard deviation1294.3136
Coefficient of variation (CV)5.0351593
Kurtosis637.41982
Mean257.05514
Median Absolute Deviation (MAD)31
Skewness21.146849
Sum12321938
Variance1675247.6
MonotonicityNot monotonic
2023-04-14T22:53:38.092865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 1396
 
2.9%
17 1363
 
2.8%
18 1227
 
2.6%
19 1206
 
2.5%
20 1079
 
2.3%
21 1013
 
2.1%
22 932
 
1.9%
23 928
 
1.9%
24 867
 
1.8%
25 803
 
1.7%
Other values (2438) 37121
77.4%
ValueCountFrequency (%)
16 1396
2.9%
17 1363
2.8%
18 1227
2.6%
19 1206
2.5%
20 1079
2.3%
21 1013
2.1%
22 932
1.9%
23 928
1.9%
24 867
1.8%
25 803
1.7%
ValueCountFrequency (%)
69448 1
< 0.1%
57126 1
< 0.1%
48011 1
< 0.1%
44503 1
< 0.1%
41852 1
< 0.1%
41785 1
< 0.1%
39941 1
< 0.1%
38639 1
< 0.1%
38097 1
< 0.1%
37877 1
< 0.1%

ae_clicks
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct481
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.502514
Minimum0
Maximum6853
Zeros14839
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size374.6 KiB
2023-04-14T22:53:38.309240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile54
Maximum6853
Range6853
Interquartile range (IQR)7

Descriptive statistics

Standard deviation73.922916
Coefficient of variation (CV)5.4747521
Kurtosis2442.3768
Mean13.502514
Median Absolute Deviation (MAD)2
Skewness38.077571
Sum647243
Variance5464.5975
MonotonicityNot monotonic
2023-04-14T22:53:38.486766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14839
31.0%
1 6651
13.9%
2 4371
 
9.1%
3 3168
 
6.6%
4 2428
 
5.1%
5 1832
 
3.8%
6 1540
 
3.2%
7 1292
 
2.7%
8 1043
 
2.2%
9 853
 
1.8%
Other values (471) 9918
20.7%
ValueCountFrequency (%)
0 14839
31.0%
1 6651
13.9%
2 4371
 
9.1%
3 3168
 
6.6%
4 2428
 
5.1%
5 1832
 
3.8%
6 1540
 
3.2%
7 1292
 
2.7%
8 1043
 
2.2%
9 853
 
1.8%
ValueCountFrequency (%)
6853 1
< 0.1%
4415 1
< 0.1%
4227 1
< 0.1%
3537 1
< 0.1%
3310 1
< 0.1%
2872 1
< 0.1%
2851 1
< 0.1%
2748 1
< 0.1%
2291 1
< 0.1%
2247 1
< 0.1%

ae_revenue
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct43914
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0983199
Minimum0.000111
Maximum2137.1819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size374.6 KiB
2023-04-14T22:53:38.672307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.000111
5-th percentile0.0036861
Q10.0260465
median0.112968
Q30.537878
95-th percentile4.7054774
Maximum2137.1819
Range2137.1818
Interquartile range (IQR)0.5118315

Descriptive statistics

Standard deviation27.784254
Coefficient of variation (CV)13.24119
Kurtosis2246.4576
Mean2.0983199
Median Absolute Deviation (MAD)0.104059
Skewness42.191015
Sum100582.97
Variance771.96476
MonotonicityNot monotonic
2023-04-14T22:53:38.840817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.006658 6
 
< 0.1%
0.002566 6
 
< 0.1%
0.002842 6
 
< 0.1%
0.00445 5
 
< 0.1%
0.002506 5
 
< 0.1%
0.006756 5
 
< 0.1%
0.005598 5
 
< 0.1%
0.003386 5
 
< 0.1%
0.003614 5
 
< 0.1%
0.002626 5
 
< 0.1%
Other values (43904) 47882
99.9%
ValueCountFrequency (%)
0.000111 1
< 0.1%
0.000122 1
< 0.1%
0.000134 1
< 0.1%
0.000143 1
< 0.1%
0.000154 1
< 0.1%
0.000159 1
< 0.1%
0.000162 1
< 0.1%
0.000167 1
< 0.1%
0.000169 1
< 0.1%
0.000177 1
< 0.1%
ValueCountFrequency (%)
2137.181915 1
< 0.1%
1860.866497 1
< 0.1%
1641.986936 1
< 0.1%
1457.692016 1
< 0.1%
1358.036254 1
< 0.1%
1327.266005 1
< 0.1%
1296.540283 1
< 0.1%
1241.249077 1
< 0.1%
954.945284 1
< 0.1%
925.251211 1
< 0.1%

Unnamed: 10
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing47934
Missing (%)> 99.9%
Memory size374.6 KiB
#NAME?

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row#NAME?

Common Values

ValueCountFrequency (%)
#NAME? 1
 
< 0.1%
(Missing) 47934
> 99.9%

Length

2023-04-14T22:53:38.993410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-14T22:53:39.217400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
name 1
100.0%

Most occurring characters

ValueCountFrequency (%)
# 1
16.7%
N 1
16.7%
A 1
16.7%
M 1
16.7%
E 1
16.7%
? 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1
25.0%
A 1
25.0%
M 1
25.0%
E 1
25.0%
Other Punctuation
ValueCountFrequency (%)
# 1
50.0%
? 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4
66.7%
Common 2
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1
25.0%
A 1
25.0%
M 1
25.0%
E 1
25.0%
Common
ValueCountFrequency (%)
# 1
50.0%
? 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
# 1
16.7%
N 1
16.7%
A 1
16.7%
M 1
16.7%
E 1
16.7%
? 1
16.7%

Unnamed: 11
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing47934
Missing (%)> 99.9%
Memory size374.6 KiB
#NAME?

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row#NAME?

Common Values

ValueCountFrequency (%)
#NAME? 1
 
< 0.1%
(Missing) 47934
> 99.9%

Length

2023-04-14T22:53:39.409890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-14T22:53:39.603365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
name 1
100.0%

Most occurring characters

ValueCountFrequency (%)
# 1
16.7%
N 1
16.7%
A 1
16.7%
M 1
16.7%
E 1
16.7%
? 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1
25.0%
A 1
25.0%
M 1
25.0%
E 1
25.0%
Other Punctuation
ValueCountFrequency (%)
# 1
50.0%
? 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4
66.7%
Common 2
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1
25.0%
A 1
25.0%
M 1
25.0%
E 1
25.0%
Common
ValueCountFrequency (%)
# 1
50.0%
? 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
# 1
16.7%
N 1
16.7%
A 1
16.7%
M 1
16.7%
E 1
16.7%
? 1
16.7%

Unnamed: 12
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing47935
Missing (%)100.0%
Memory size374.6 KiB

Unnamed: 13
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct6
Distinct (%)100.0%
Missing47929
Missing (%)> 99.9%
Memory size374.6 KiB
b1
b2
b3
b4
b5

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters12
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)100.0%

Sample

1st rowb1
2nd rowb2
3rd rowb3
4th rowb4
5th rowb5

Common Values

ValueCountFrequency (%)
b1 1
 
< 0.1%
b2 1
 
< 0.1%
b3 1
 
< 0.1%
b4 1
 
< 0.1%
b5 1
 
< 0.1%
b6 1
 
< 0.1%
(Missing) 47929
> 99.9%

Length

2023-04-14T22:53:39.766929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-14T22:53:39.953430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
b1 1
16.7%
b2 1
16.7%
b3 1
16.7%
b4 1
16.7%
b5 1
16.7%
b6 1
16.7%

Most occurring characters

ValueCountFrequency (%)
b 6
50.0%
1 1
 
8.3%
2 1
 
8.3%
3 1
 
8.3%
4 1
 
8.3%
5 1
 
8.3%
6 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6
50.0%
Decimal Number 6
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
16.7%
2 1
16.7%
3 1
16.7%
4 1
16.7%
5 1
16.7%
6 1
16.7%
Lowercase Letter
ValueCountFrequency (%)
b 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6
50.0%
Common 6
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
16.7%
2 1
16.7%
3 1
16.7%
4 1
16.7%
5 1
16.7%
6 1
16.7%
Latin
ValueCountFrequency (%)
b 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 6
50.0%
1 1
 
8.3%
2 1
 
8.3%
3 1
 
8.3%
4 1
 
8.3%
5 1
 
8.3%
6 1
 
8.3%

Unnamed: 14
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct5
Distinct (%)100.0%
Missing47930
Missing (%)> 99.9%
Memory size374.6 KiB
1.0
100.0
300.0
600.0
1500.0

Length

Max length6
Median length5
Mean length4.8
Min length3

Characters and Unicode

Total characters24
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row1.0
2nd row100.0
3rd row300.0
4th row600.0
5th row1500.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
100.0 1
 
< 0.1%
300.0 1
 
< 0.1%
600.0 1
 
< 0.1%
1500.0 1
 
< 0.1%
(Missing) 47930
> 99.9%

Length

2023-04-14T22:53:40.120021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-14T22:53:40.347375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
20.0%
100.0 1
20.0%
300.0 1
20.0%
600.0 1
20.0%
1500.0 1
20.0%

Most occurring characters

ValueCountFrequency (%)
0 13
54.2%
. 5
 
20.8%
1 3
 
12.5%
3 1
 
4.2%
6 1
 
4.2%
5 1
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19
79.2%
Other Punctuation 5
 
20.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13
68.4%
1 3
 
15.8%
3 1
 
5.3%
6 1
 
5.3%
5 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13
54.2%
. 5
 
20.8%
1 3
 
12.5%
3 1
 
4.2%
6 1
 
4.2%
5 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13
54.2%
. 5
 
20.8%
1 3
 
12.5%
3 1
 
4.2%
6 1
 
4.2%
5 1
 
4.2%

Unnamed: 15
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)100.0%
Missing47929
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean773
Minimum0
Maximum2138
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size374.6 KiB
2023-04-14T22:53:40.473038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q1150
median450
Q31275
95-th percentile1978.5
Maximum2138
Range2138
Interquartile range (IQR)1125

Descriptive statistics

Standard deviation859.75229
Coefficient of variation (CV)1.1122281
Kurtosis-0.6588904
Mean773
Median Absolute Deviation (MAD)400
Skewness0.96765175
Sum4638
Variance739174
MonotonicityStrictly increasing
2023-04-14T22:53:40.577797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1
 
< 0.1%
100 1
 
< 0.1%
300 1
 
< 0.1%
600 1
 
< 0.1%
1500 1
 
< 0.1%
2138 1
 
< 0.1%
(Missing) 47929
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
100 1
< 0.1%
300 1
< 0.1%
600 1
< 0.1%
1500 1
< 0.1%
2138 1
< 0.1%
ValueCountFrequency (%)
2138 1
< 0.1%
1500 1
< 0.1%
600 1
< 0.1%
300 1
< 0.1%
100 1
< 0.1%
0 1
< 0.1%

Interactions

2023-04-14T22:53:32.663523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:18.140539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:20.241674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:22.106759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:23.760334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:25.451811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:27.312833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:28.885625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:30.892281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:32.788185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:18.714785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:20.409263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:22.301240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:23.954778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:25.625310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:27.485377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:29.065109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:31.092720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:32.919796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:18.887326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:20.582764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:22.490693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:24.216082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:25.806859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:27.663894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:29.343402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:31.264224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:33.057428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:19.069801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:20.764349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:22.683178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:24.426552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:26.002302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:27.879318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:29.571753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:31.458744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:33.171128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:19.248321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:21.002675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:22.875663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:24.614050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:26.204759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:28.062791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:29.870991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:31.650218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:33.286852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:19.421860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:21.228116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:23.067190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:24.802548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:26.411246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:28.251323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:30.169192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:31.886559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:33.397557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:19.590450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:21.445533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:23.255648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:24.987054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:26.813132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:28.428811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:30.392561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:32.195772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:33.523182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:19.755006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:21.770658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:23.453119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:25.169529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:27.020577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:28.606336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:30.597048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:32.399190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:33.642864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:19.899616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:21.942198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:23.603718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:25.320161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:27.178191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:28.756935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:30.752593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-14T22:53:32.552815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-14T22:53:40.777226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
pub_idapp_idad_unit_coderequestsae_responsesae_impressionsae_clicksae_revenueUnnamed: 15dateUnnamed: 13Unnamed: 14
pub_id1.0000.072-0.389-0.167-0.157-0.196-0.308-0.205NaN0.1101.0001.000
app_id0.0721.000-0.087-0.310-0.299-0.187-0.226-0.303NaN0.0811.0001.000
ad_unit_code-0.389-0.0871.0000.0970.1240.0830.0250.104-0.7170.0871.0001.000
requests-0.167-0.3100.0971.0000.9860.9050.4670.478-0.0860.0001.0001.000
ae_responses-0.157-0.2990.1240.9861.0000.9190.4840.506-0.1430.0001.0001.000
ae_impressions-0.196-0.1870.0830.9050.9191.0000.5250.566-0.0860.0001.0001.000
ae_clicks-0.308-0.2260.0250.4670.4840.5251.0000.7060.0000.0081.0001.000
ae_revenue-0.205-0.3030.1040.4780.5060.5660.7061.000-0.0290.0001.0001.000
Unnamed: 15NaNNaN-0.717-0.086-0.143-0.0860.000-0.0291.0001.0001.0001.000
date0.1100.0810.0870.0000.0000.0000.0080.0001.0001.0001.0001.000
Unnamed: 131.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 141.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-04-14T22:53:33.857324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-14T22:53:34.302147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-14T22:53:35.110151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

datepub_idapp_idad_unit_codecountryrequestsae_responsesae_impressionsae_clicksae_revenueUnnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15
020-10-202219871624327022832846443Tanzania36363400.035738#NAME?NaNNaNNaNNaNNaN
120-10-202219871624327022832846593Tanzania24242300.064384NaN#NAME?NaNb1NaN0.0
220-10-202219871624327022832846443Thailand949949876204.943878NaNNaNNaNb21.0100.0
320-10-202219871624327022832846593Thailand100690768813424.226836NaNNaNNaNb3100.0300.0
420-10-202219871624327022832827729Thailand373287156202.759392NaNNaNNaNb4300.0600.0
520-10-202219871624327022832846443United States22522521224.316628NaNNaNNaNb5600.01500.0
620-10-202219871624327022832827729United States39393021.450446NaNNaNNaNb61500.02138.0
720-10-202219871624327022832846593United States189189165420.327859NaNNaNNaNNaNNaNNaN
820-10-202219871624327022832846593United Kingdom33332511.874777NaNNaNNaNNaNNaNNaN
920-10-202219871624327022832846443United Kingdom37373610.269845NaNNaNNaNNaNNaNNaN
datepub_idapp_idad_unit_codecountryrequestsae_responsesae_impressionsae_clicksae_revenueUnnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15
4792522-10-2022149567477122770341598Venezuela20201930.035682NaNNaNNaNNaNNaNNaN
4792622-10-2022149567477122770341049Albania29292050.082596NaNNaNNaNNaNNaNNaN
4792722-10-2022149567477122770341049Peru40402020.076515NaNNaNNaNNaNNaNNaN
4792822-10-2022149567477122770341049Philippines40362040.112624NaNNaNNaNNaNNaNNaN
4792922-10-2022149567477122770341049Switzerland37372031.466152NaNNaNNaNNaNNaNNaN
4793022-10-2022149567477122770341049El Salvador34342140.022347NaNNaNNaNNaNNaNNaN
4793122-10-2022149567477122770341430Jordan34342100.011894NaNNaNNaNNaNNaNNaN
4793222-10-2022149567477122770341430Somalia46462120.003189NaNNaNNaNNaNNaNNaN
4793322-10-2022149567477122770341598Portugal21212150.159408NaNNaNNaNNaNNaNNaN
4793422-10-2022149567477122770341049Thailand40402110.140938NaNNaNNaNNaNNaNNaN